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Deep learning methods achieve great success recently on many computer vision problems, with image classification and object detection as the prominent examples.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E. Hinton · 2010
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Network in network
Min Lin, Qiang Chen, and Shuicheng Yan · 2013
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On the difficulty of training recurrent neural networks
Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio · 2013
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On the importance of initialization and momentum in deep learning
Ilya Sutskever, James Martens, George E. Dahl, and Geoffrey E. Hinton · 2013
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Provable bounds for learning some deep representations
Sanjeev Arora, Aditya Bhaskara, Rong Ge, and Tengyu Ma · 2014
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Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
Yann N. Dauphin, Razvan Pascanu, Çaglar Gülçehre, KyungHyun Cho, Surya Ganguli, and Yoshua Bengio · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick · 2014
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Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
Andrew M. Saxe, James L. McClelland, and Surya Ganguli · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Deeply-supervised nets
Chen-Yu Lee, Saining Xie, Patrick W. Gallagher, Zhengyou Zhang, and Zhuowen Tu · 2015
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Dmytro Mishkin and Jiri Matas · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, and Kilian Q. Weinberger · 2016
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Deep networks with stochastic depth
Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, and Kilian Weinberger · 2016
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Deep learning without poor local minima
Kenji Kawaguchi · 2016
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Optimization on submanifolds of convolution kernels in cnns
Mete Ozay and Takayuki Okatani · 2016
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Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
Cited alongside, same era.
Fitnets: Hints for thin deep nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2015
Cited alongside, same era.
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
Cited alongside, same era.
Rupesh Kumar Srivastava, Klaus Greff, and Jürgen Schmidhuber · 2015
Cited alongside, same era.
Unitary evolution recurrent neural networks
Martin Arjovsky, Amar Shah, and Yoshua Bengio · 2016
Cited alongside, same era.
Towards optimality conditions for non-linear networks
Devansh Arpit, Hung Q. Ngo, Yingbo Zhou, Nils Napp, and Venu Govindaraju · 2016
Cited alongside, same era.
Tim Salimans and Diederik P. Kingma · 2016
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Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alex A. Alemi · 2016
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Analysis of deep neural networks with extended data jacobian matrix
Shengjie Wang, Abdel-rahman Mohamed, Rich Caruana, Jeff A. Bilmes, Matthai Philipose, Matthew Richardson, Krzysztof Geras, Gregor Urban, and Özlem Aslan · 2016
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Full-capacity unitary recurrent neural networks
Scott Wisdom, Thomas Powers, John R. Hershey, Jonathan Le Roux, and Les Atlas · 2016
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Sergey Zagoruyko and Nikos Komodakis · 2016
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Residual networks of residual networks: Multilevel residual networks
Ke Zhang, Miao Sun, Tony X. Han, Xingfang Yuan, Liru Guo, and Tao Liu · 2016
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